Figure REF  depicts the extent to which the number of local models affects the performance of LOCA\(_{VAE}\) . LLORMA\(_{VAE}\)  builds and aggregates local models based on LLORMA [1]}, [2]}; however, it adopts MultVAE [3]} as its base model instead of MF. It was observed that the accuracy of LOCA\(_{VAE}\)  improved consistently with an increase in the number of local models. For the motivating question that we posed in Section REF , is it better to use different weights for training and aggregating local models?, our experimental results imply that the answer is yes. The simple ensemble model that assigns the same weight values for all local models and LLORMA\(_{VAE}\)  using the same weights for training and aggregating local models show a limited improvement, converging to the best NDCG@100 with 50 local models. By contrast, LOCA\(_{VAE}\)  improves beyond this level by taking advantage of up to 300 newly added local models. From this observation, we conclude that LOCA\(_{VAE}\)  is more effective in capturing local sub-communities. One might wonder whether this conclusion would hold if we had as many local models the number of users. Developing more local models does not impair the accuracy, but the performance gains diminish.
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